PROJECTSUMMARY Heartfailure(HF)isahighlydisablingandcostlydiseasewithahighmortalityrate.Intheprediagnosticphase (i.e., 1236 months before diagnosis), HF is difficult to detect given the insidious signs and symptoms. After diagnosis, where it isnotpossibletoreversediseaseprogression,effortsaremadetoavoidhospitaladmission and readmission, but with limited capabilities to stratify patients by risk. We propose to develop interpretable deeplearningmodelsappliedtolargescaleelectronichealthrecord(EHR)datatodetectHFrelatedeventson two different time scales. One set of modelswillbedevelopedtodetectHFdiagnosisonetotwoyearsbefore actual documented diagnosis. Separately, we propose to identify HF patients who are at risk of hospital admission and readmission.Theprojectfocusesondevelopingdeeplearningmodelsthatofferthepotentialfor greater accuracy, clinical interpretability, and utility than alternatives. The expected deliverables include comprehensive software for creating deep learning algorithms that predict HF outcomes and related software toolsformodelvisualization.